Improving primary healthcare access for asylum seekers and refugees: a qualitative study from a swiss family physician perspective

Since 2015 the need for evidence-based guidance in primary health care management of refugees, asylum seekers, and immigrants has dramatically increased. The aims of this study were to identify the challenges met by primary care physicians in Switzerland, by performing semi-structured interviews and to identify possible approaches and interventions. Between January 2019 and January 2020, 20 GPs in 3 Swiss cantons were interviewed. The interviews were transcribed, coded with MAXQDA 18, and analyzed using the framework methodology. Following relevant findings were highlighted; (i) problems relating to health insurance companies among (health-insured) asylum seekers and refugees were negligible; (ii) there is a high acceptance for vaccination by refugees, asylum seekers, and immigrants; (iii) limitations in time for consultations and adequate reimbursement for practitioners pose a serious challenge; (iv) the majority of consultations are complaint-oriented, preventive consultations are rare; and (v) the language barrier is a major challenge for psychosocial consultations, whereas this appears less relevant for somatic complaints. The following issues were identified as high priority needs by the study participants; (i) increased networking between GPs, that is, establishing bridging services with asylum centers, (ii) improved training opportunities for GPs in Migration Medicine with regular updates of current guidelines, and (iii) a standardisation of health documentation facilitating exchange of medical data, that is, digital/paper-based "health booklet" or "health pass.

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994-2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm(-2)) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.An international team of researchers finds high potential for improving climate projections by a more comprehensive treatment of largely ignored Arctic vegetation types, underscoring the importance of Arctic energy exchange measuring stations.Peer reviewe

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types

Effects of growth hormone and IGF-I on glucocorticoid-induced protein catabolism in humans

The effects of similar increases in total insulin-like growth factor I (IGF-I) plasma concentrations achieved by either recombinant human (rh) growth hormone (GH) or rhIGF-I administration on whole body protein and glucose kinetics were assessed. Twenty-six healthy subjects received methylprednisolone (0.5 mg.kg-1.day-1 orally) during 6 days in combination with either placebo (saline sc), GH (0.3 mg.kg-1.day-1 sc), or IGF-I (80 micrograms.kg-1.day-1 sc) in a double-blind randomized fashion. Glucocorticoid administration resulted in protein catabolism as indicated by an increase in leucine flux and a 62 +/- 13% increase in leucine oxidation ([1-13C]leucine infusion technique); this increase was abolished by GH (-1 +/- 18%) as was statistically insignificant during IGF-I treatment (+53 +/- 25%). GH increased endogenous glucose production by 28 +/- 8%, augmented glucocorticoid-induced insulin resistance of peripheral glucose clearance (euglycemic clamp), and increased circulating lipids. IGF-I administration resulted in both increased endogenous glucose production and increased peripheral glucose clearance such that plasma glucose concentrations remained unchanged by IGF-I. IGF-I lowered circulating GH and insulin and altered IGF binding proteins, which all may have reduced bioactivity of IGF-I. The data demonstrate that, in spite of similar total IGF-I plasma concentrations during treatment, GH and IGF-I exert markedly different effects on whole body leucine, glucose, and lipid metabolism

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types. © 2022, The Author(s)

Polymorphonuclear neutrophil-derived ectosomes interfere with the maturation of monocyte-derived dendritic cells

Polymorphonuclear neutrophils (PMNs) are a key component of the innate immune system. Their activation leads to the release of potent antimicrobial agents through degranulation. Simultaneously, PMNs release cell surface-derived microvesicles, so-called ectosomes (PMN-Ect). PMN-Ect are rightside-out vesicles with a diameter of 50-200 nm. They expose phosphatidylserine in the outer leaflet of their membrane and down-modulate monocyte/macrophage-activation in vitro. In this study, we analyzed the effects of PMN-Ect on maturation of human monocyte-derived dendritic cells (MoDCs). Intriguingly, exposing immature MoDCs to PMN-Ect modified their morphology, reduced their phagocytic activity, and increased the release of TGF-beta1. When immature MoDCs were incubated with PMN-Ect and stimulated with the TLR4 ligand LPS, the maturation process was partially inhibited as evidenced by reduced expression of cell surface markers (CD40, CD80, CD83, CD86, and HLA-DP DQ DR), inhibition of cytokine-release (IL-8, IL-10, IL-12, and TNF-alpha), and a reduced capacity to induce T cell proliferation. Together these data provide evidence that PMN-Ect have the ability to modify MoDC maturation and function. PMN-Ect may thus represent an as yet unidentified host-factor influencing MoDC maturation at the site of injury, thereby possibly impacting on downstream MoDC-dependent immunity

Use of artificial intelligence to assess the risk of coronary artery disease without additional (non-invasive) testing:validation in a low-risk to intermediate-risk outpatient clinic cohort

OBJECTIVES: Predicting the presence or absence of coronary artery disease (CAD) is clinically important. Pretest probability (PTP) and CAD consortium clinical (CAD2) model and risk scores used in the guidelines are not sufficiently accurate as the only guidance for applying invasive testing or discharging a patient. Artificial intelligence without the need of additional non-invasive testing is not yet used in this context, as previous results of the model are promising, but available in high-risk population only. Still, validation in low-risk patients, which is clinically most relevant, is lacking. DESIGN: Retrospective cohort study. SETTING: Secondary outpatient clinic care in one Dutch academic hospital. PARTICIPANTS: We included 696 patients referred from primary care for further testing regarding the presence or absence of CAD. The results were compared with PTP and CAD2 using receiver operating characteristic (ROC) curves (area under the curve (AUC)). CAD was defined by a coronary stenosis >50% in at least one coronary vessel in invasive coronary or CT angiography, or having a coronary event within 6 months. OUTCOME MEASURES: The first cohort validating the memetic pattern-based algorithm (MPA) model developed in two high-risk populations in a low-risk to intermediate-risk cohort to improve risk stratification for non-invasive diagnosis of the presence or absence of CAD. RESULTS: The population contained 49% male, average age was 65.6±12.6 years. 16.2% had CAD. The AUCs of the MPA model, the PTP and the CAD2 were 0.87, 0.80, and 0.82, respectively. Applying the MPA model resulted in possible discharge of 67.7% of the patients with an acceptable CAD rate of 4.2%. CONCLUSIONS: In this low-risk to intermediate-risk population, the MPA model provides a good risk stratification of presence or absence of CAD with a better ROC compared with traditional risk scores. The results are promising but need prospective confirmation